1. Field of the Invention
The invention pertains to apparatus and methods for monitoring cables or electrical conduits to detect tampering. More particularly, the invention pertains to tamper detection using a distributed capacitance or resistance sensing circuit followed by spectral analysis of the sensor data.
2. Description of Related Art
In the field of communications and physical security, there is often a need to detect intrusions, for example, through a perimeter fence or the like, as well as detect tampering with a cable that might be carrying sensitive data. Such systems traditionally function by defining “normal” versus “alarm” states in terms of some threshold value of one or more parameters. In the simplest case, a trip wire or window alarm simply detects the breakage of electrical continuity of a circuit loop. Motion detectors based on ultrasonics, active infrared, or passive infrared detect changes in an incoming or reflected audio or optical signal and trigger an alarm when the magnitude of the signal exceeds some threshold. Such devices usually have some form of “sensitivity” control, which adjusts the threshold level in an effort to minimize false alarms.
It is well known that electrical cables and conduits may display minute electrical changes in response to physical contact, movement, or vibration. Cables can be constructed to enhance such “microphonic” effects by, for instance, adding materials with large triboresistive coefficients such as taught by Maki in U.S. Pat. No. 6,967,584, the entire disclosure of which is incorporated herein by reference.
One system that exploits this approach is the E-Flex 3i Interior Security System, [GE Interlogix UK, Unit 5, Ashton Gate, Ashton Road, Harold Hill, Romford, Essex RM3 8UF, England]. It was developed for use as an intruder detection system for building interiors. It uses a “strain sensitive” cable to detect vibrations of surfaces where the cable is installed, including walls, ceilings, floors and pipes. The cable is attached to a signal processor unit that monitors the cable electrical signal and compares the signal magnitude to a threshold level. If the signal magnitude exceeds the threshold level, an “event” is detected and indicated. Several controls are provided by that system. These include: (a) Signal frequency band control—used to filter either low-frequency or high-frequency noise from the signal prior to comparison against the threshold level. (b) Sensitivity control—used to vary the threshold level. (c) Event counter control—used to count the events detected by the system. (d) Time window control—used in conjunction with the event counter control to set the time interval during which events are counted. The primary method used by the system to detect intruders appears to rely on counting the number of times the magnitude of a pre-filtered signal exceeds a preset threshold within a preset time period.
Another commercial product that uses a capacitive sensor cable to detect intruders is the Fence Protection Systems from Perimeter Products, Inc. [now called Magal-Senstar, Inc. 43180 Osgood Rd., Fremont, Calif. 94539]. Magal-Senstar literature states that climbing produces low-frequency noise, while cutting produces high-frequency noise. The total signal bandwidth analyzed by that system is from 80 Hz to 3 kHz.
In many situations a conduit to be monitored will be subject to various extraneous physical vibrations, accidental impacts, etc. In order to minimize the occurrence of false alarms, it is necessary for the monitor to be able to reliably distinguish between benign and suspicious signals and to do so with minimal operator intervention. In most instances systems are designed with very general parameter sets that enable them to reject the most commonly encountered types of noise signals while reliably detecting intrusion or tampering events. Inevitably, the generality of this process creates a lack of precision in distinguishing between noise and intrusion signals. As a result, parameters must be set low enough to avoid an excessive number of false alarms yet high enough to provide a good probability of detecting invasive activities. Because noise sources can be highly specific to a given location or installation, it would be useful for the systems to be able to specifically recognize (through a learning process) the known noise sources that may be associated with a given installation. This would enable more rigorous detection of invasive activities and more robust rejection of known noise signals.